Imagine a Texan firing randomly at the side of a barn, then painting bullseyes around the bullet holes. Every shot becomes a perfect hit. It sounds absurd, yet we do something remarkably similar with information every day. We look at messy data, spot clusters that seem meaningful, and convince ourselves we've discovered something real.

This mental habit—finding patterns after the fact and treating them as predictions—is called the Texas Sharpshooter Fallacy. It explains why people believe in biorhythms, why stock market gurus seem prescient, and why that psychic who predicted a celebrity death seems genuinely gifted. Understanding this fallacy is your first line of defense against self-deception.

The core trick of the Texas Sharpshooter is simple: define your pattern after you've seen the data. Suppose you notice that three of your headaches occurred on rainy days. It feels like a discovery. Rain causes headaches! But you've forgotten the many rainy days without headaches and the sunny days when your head pounded.

This happens because our brains are pattern-recognition machines evolved to find meaning everywhere. Spotting a tiger in the grass once saved our ancestors' lives. But this same instinct now makes us see faces in clouds, messages in coincidences, and significance in random clustering. The brain doesn't distinguish between patterns we predicted beforehand and patterns we noticed afterward.

Consider cancer cluster investigations. When residents notice several neighbors diagnosed with cancer, it feels impossible that this could be random. But with millions of neighborhoods and thousands of cancer cases, some clustering is mathematically guaranteed. The pattern is real; the meaning we assign to it often isn't. True investigation requires asking whether the cluster exceeds what chance alone would produce—not whether a cluster exists at all.

Takeaway

A pattern noticed after the fact isn't evidence of anything except your brain's pattern-seeking nature. Real discoveries require predictions made before looking at the data.

Here's a statistical truth that feels wrong: if you look at enough data, you will always find something significant. Run twenty different statistical tests, and you'll likely get one striking result purely by chance. This isn't bad luck—it's mathematical certainty. Scientists call it the multiple comparisons problem.

Think of it like rolling dice. Getting three sixes in a row seems remarkable. But if you roll dice thousands of times, you're guaranteed to see it eventually. The Texas Sharpshooter reports the three sixes while hiding the thousands of ordinary rolls. Diet studies fall into this trap constantly. Researchers measure dozens of health outcomes, find that one improves, and announce a breakthrough—while quietly ignoring everything that showed no effect.

This is why preregistration has become essential in scientific research. Scientists now publicly declare exactly what they're testing before collecting data. This prevents the temptation to hunt through results for something—anything—that looks impressive. When you see a study, ask: did they decide what to measure before or after seeing results? The answer often separates genuine findings from statistical mirages.

Takeaway

The more comparisons you make, the more likely you'll find something by pure chance. Impressive-looking results mean little without knowing how many things were tested to find them.

The antidote to Texas Sharpshooter thinking is deceptively simple: decide what counts as evidence before you look. A prediction made before examining data means something completely different from a pattern found afterward. One can be tested; the other cannot.

This is why scientists distinguish between exploratory research and confirmatory research. Exploration generates hypotheses—interesting patterns worth investigating. Confirmation tests those hypotheses on fresh data. Both are valuable, but only confirmation actually proves anything. The problem arises when exploration masquerades as confirmation, when patterns found by searching are presented as patterns that were predicted.

You can apply this principle personally. Before checking whether your new productivity system works, define success. How much more will you accomplish? By when? Write it down. Without predetermined criteria, you'll unconsciously adjust your definition of success to match whatever happened. This isn't dishonesty—it's human nature. We all want to believe our efforts work. Predetermined standards protect us from our own motivated reasoning.

Takeaway

Write down your predictions before gathering evidence. If you can't specify what would change your mind beforehand, you're not really testing anything—you're just confirming what you already believe.

Our pattern-seeking minds are both our greatest tool and our most persistent source of error. The Texas Sharpshooter Fallacy reminds us that finding patterns requires no skill—randomness produces patterns everywhere. The skill lies in distinguishing meaningful patterns from coincidental ones.

Before you believe any striking pattern or correlation, ask three questions: Was this pattern predicted beforehand? How many other patterns were examined to find this one? Could this clustering happen by chance alone? These simple questions separate genuine insight from painted-on bullseyes.